Asset Allocation in a Low Yield Environment
John Huss (AQR Capital Mgt.), et al.
August 17, 2017
The year 2016 saw bond yields fall to unprecedented low levels in major developed markets, with nominal yields on 10-year German and Japanese government bonds even turning negative. While yields have risen off their lows in 2017, we are still in a very low rate environment. Does this demand exceptional action from investors – even those who usually maintain a strategic allocation to global bonds? We find that it does not, instead it highlights the importance of diversification across many return sources.

Breadth Momentum and Vigilant Asset Allocation: Winning More by Losing Less
Wouter J. Keller (VU University Amsterdam) and J.W. Keuning (TrendXplorer)
July 14, 2017
VAA (Vigilant Asset Allocation) is a dual-momentum based investment strategy with a vigorous crash protection and a fast momentum filter. Dual momentum combines absolute (trendfollowing) and relative (strength) momentum. Compared to the traditional dual momentum approaches, we have replaced the usual crash protection through trendfollowing on the asset level by our breadth momentum on the universe level instead. As a result, the VAA strategy is on average often more than 50% out of the market. We show, however, that the resulting momentum strategy is by no means sluggish. By using large and small universes with US and global ETF-like monthly data starting 1925 and 1969 respectively, we arrive out-of-sample at annual returns above 10% with max drawdowns below 15% for each of these four universes.

Timing the Market with a Combination of Moving Averages
Paskalis Glabadanidis (University of Adelaide Business School)
September 2017
A combination of simple moving average trading strategies with several window lengths delivers a greater average return and skewness as well as a lower variance and kurtosis compared with buying and holding the underlying asset using daily returns of value‐weighted US decile portfolios sorted by market size, book‐to‐market, momentum, and standard deviation as well as more than 1000 individual US stocks. The combination moving average (CMA) strategy generates risk‐adjusted returns of 2% to 16% per year before transaction costs. The performance of the CMA strategy is driven largely by the volatility of stock returns and resembles the payoffs of an at‐the‐money protective put on the underlying buy‐and‐hold return. Conditional factor models with macroeconomic variables, especially the market dividend yield, short‐term interest rates, and market conditions, can explain some of the abnormal returns. Standard market timing tests reveal ample evidence regarding the timing ability of the CMA strategy.

How Many Factors? Does Adding Momentum and Volatility Improve Performance
Mohammed Elgammal (Qatar University), et al.
August 11, 2017
This paper considers whether adding two established anomalies, momentum and low volatility, will improve our understanding of asset pricing beyond the five-factor model (FF5). We do this by considering whether these factors provide economic, as opposed to statistical, significance within the asset pricing model. We measure economic significance in two ways, first, we consider whether the factor coefficient signs and values on the factors are economically meaningful, for example, do the coefficients distinguish between high and low risk portfolios. Second, weGeoff Warren

What Does it Mean to Be a Long-Term Investor?
Geoff Warren (Australian National University)
July 2016
It is worthwhile noting upfront that both short-term and long-term investors have roles to play, and can be successful. However, they approach investing in different ways, aiming to exploit different advantages. An overarching benefit of long-term investing is access to a broader opportunity set. Long-term investors can pursue a wide range of investments and strategies, including those available to short-term investors, although in practice they may choose not to do so. Long-term investors are more likely to be successful where they exploit advantages that stem from possessing discretion over when they trade, and a tolerance for short-term volatility or near-term underperformance. One such advantage is the capacity to adopt positions where payoff timing is uncertain, e.g., investing where long-term value exists, but may not be recognized by the market anytime soon, or to capture themes that unfold over time. Another advantage is the ability to capture opportunities that arise from the actions of short-term investors, perhaps because they are required to trade or are being short-sighted in their evaluations. Examples include contrarian investing, standing as the buyer of last resort in a crisis, and harvesting risk premiums that arise from the fears of those with shorter horizons. A further advantage is the scope to invest in illiquid assets. This offers return and diversification benefits through widening the range of accessible investments. Against this background, the question arises: what characterizes a “long-term investor?”

Rebalancing for Long Term Investors
Joost Driessen and Ivo Kuiper (Tilburg University)
May 29, 2017
In this study we show that the rebalance frequency of a multi-asset portfolio has only limited impact on the utility of a long term passive investor. Although continuous rebalancing is optimal, the loss of a suboptimal strategy corresponds to up to only 30 basis points of the initial wealth of the investor, assuming market returns are unpredictable and transaction costs can be ignored. Our results suggest that reducing transaction costs clearly outweighs the benefit of frequent rebalancing. We also show that by ignoring market return predictability, the investor underestimates the utility gain of less frequent rebalancing. In this setting, limiting the frequency to once every quarter results in significant higher utility, even without transaction costs.

Sin Stocks Revisited: Resolving the Sin Stock Anomaly
David Blitz (Robeco Asset Mgt.) and Frank J. Fabozzi (EDHEC Business School)
August 9, 2017
Various studies report that investing in “sin stocks”, that is firms which make money from human vice, such as alcohol, tobacco, gambling and weapons, has historically delivered significantly positive abnormal returns. This finding has inspired the hypothesis that sin stocks are being shunned to such an extent that they end up being systematically underpriced, enabling other investors, who are willing to bear the reputation risk involved with investing in these stocks, to earn a return premium. In this article, the authors further investigate this notion, finding that the performance of sin stocks can be fully explained by the two new quality factors in the recently introduced Fama-French 5-factor model, profitability and investment. Their finding is robust over time and across different markets. In short, there is no evidence that sin stocks provide a premium for reputation risk after controlling for their exposure to factors in today’s asset pricing models.

Portfolio Optimization with Industry Return Prediction Models
W. Bessler (Justus-Liebig-University Giessen) and D. Wolff (Deka Investment)
June 30, 2017
We postulate that utilizing return prediction models with fundamental, macroeconomic, and technical indicators instead of using historical averages should result in superior asset allocation decisions. We investigate the predictive power of individual variables for forecasting industry returns in-sample and out-of-sample and then analyze multivariate predictive regression models including OLS, a regularization technique, principal components, a target-relevant latent factor approach, and forecast combinations. The gains from using industry return predictions are evaluated in an out-of-sample Black-Litterman portfolio optimization framework. We provide empirical evidence that portfolio optimization utilizing industry return prediction models significantly outperform portfolios using historical averages and those being passively managed.

Can We Use Volatility to Diagnose Financial Bubbles? Lessons from 40 Historical Bubbles
Didier Sornette (ETH Zürich), et al.
April 19, 2017
We inspect the price volatility before, during, and after financial asset bubbles in order to uncover possible commonalities and check empirically whether volatility might be used as an indicator or an early warning signal of an unsustainable price increase and the associated crash. Some researchers and finance practitioners believe that historical and/or implied volatility increase before a crash, but we do not see this as a consistent behavior. We examine forty well-known bubbles and, using creative graphical representations to capture robustly the transient dynamics of the volatility, find that the dynamics of the volatility would not have been a useful predictor of the subsequent crashes. In approximately two-third of the studied bubbles, the crash follows a period of lower volatility, reminiscent of the idiom of a “lull before the storm”. This paradoxical behavior, from the lenses of traditional asset pricing models, further questions the general relationship between risk and return.